Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive researchto promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses.

中文翻译：

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基于双四分之一扫描锚网络的双能CT重建方法

与传统的单能计算机断层扫描 (CT) 相比，双能 CT (DECT) 提供了更好的材料区分，但大多数 DECT 成像系统需要不同 X 射线光谱的双全角度投影数据。放宽数据采集的要求是一项有吸引力的研究，可以促进DECT在广泛领域的应用，并将辐射剂量降低到合理可达到的低水平。在这项工作中，我们设计了一种具有双四分之一扫描的新型 DECT 成像方案，并提出了一种从双有限角度投影数据重建所需 DECT 图像的有效方法。我们首先研究了双四分之一扫描方案下有限角度伪影的特征，发现由于高能和低能扫描对应的X射线是对称的，DECT图像的负伪影和正伪影在图像域中呈互补分布。受这一发现的启发，通过整合双四分之一扫描的有限角度 DECT 图像生成融合 CT 图像。该策略增强了真实的图像信息并抑制了有限角度伪影，从而恢复了图像边缘和内部结构。利用神经网络在非线性问题建模中的能力，本文设计了一种具有单输入双输出架构的新型 Anchor 网络，以从生成的融合 CT 图像中生成所需的 DECT 图像。模拟数据和真实数据的实验结果验证了该方法的有效性。这项工作使 DECT 能够进行半扫描成像配置，并大大减少扫描角度和辐射剂量。